Profiling of urinary extracellular vesicle protein signatures from patients with cribriform and intraductal prostate carcinoma in a cross-sectional study
Bibliographic record
Abstract
Prognostic tests and treatment approaches for optimized clinical care of prostatic neoplasms are an unmet need. Prostate cancer (PCa) and derived extracellular vesicles (EVs) proteome changes occur during initiation and progression of the disease. PCa tissue proteome has been previously characterized, but screening of tissue samples constitutes an invasive procedure. Consequently, we focused this study on liquid biopsies, such as urine samples. More specifically, urinary small extracellular vesicle and particles proteome profiles of 100 subjects were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-MS/MS). We identified 171 proteins that were differentially expressed between intraductal prostate cancer/cribriform (IDC/Crib) and non-IDC/non-Crib after correction for multiple testing. However, the strong correlation between IDC/Crib and Gleason Grade complicates the disentanglement of the underlying factors driving this association. Nevertheless, even after accounting for multiple testing and adjusting for ISUP (International Society of Urological Pathology) grading, two proteins continued to exhibit significant differential expression between IDC/Crib and non-IDC/non-Crib. Functional enrichment analysis based on cancer hallmark proteins disclosed a clear pattern of androgen response down-regulation in urinary EVs from IDC/Crib compared to non-IDC/non-Crib. Interestingly, proteome differences between IDC and cribriform were more subtle, suggesting high proteome heterogeneity. Overall, the urinary EV proteome reflected partly the prostate pathology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".